Bottom Line:
Gas Chromatography-Time of Flight/Mass Spectrometry (GC-TOF/MS) combined with multivariate and univariate analyses, and Bayesian network (BN) analysis, distinguished different tissues and confirmed the physiological switch from high rates of respiration to photosynthesis along the leaf.In plants grown in the presence of nitrate there was reduced levels of a number of sugar metabolites in the leaf base and an increase in maltose levels, possibly reflecting an increase in starch turnover.The value of using this combined metabolomics analysis for further functional investigations in the future are discussed.

Mentions:
A total of 115 metabolite features were detected by GC-TOF/MS in wheat primary leaves grown in the presence or absence of nitrate, of which a total of 51 metabolites were identified by library matching (Table S1). Chemometric analysis of the GC-TOF/MS metabolite profiles focused upon the selection of differentially expressed metabolites that revealed significant trends either between the leaf regions or in response to growing the plant in the presence or absence of nitrate. Three approaches to the data mining were applied. First multiblock Consensus (C)-PCA was applied (Biais et al., 2009; Smilde et al., 2003; Westerhuis et al., 1998; Xu and Goodacre, 2012), where models combine several different but potentially connected data sets (called “blocks”), with emphasis upon modelling the “common trend” between the blocks. The sample distribution of each individual block are shown in their respective “block scores” plot and the contribution of metabolites in relation to the observed trend are shown in their “block loadings” plot (Biais et al., 2009). The first C-PCA model (Fig. 5a and b) arranged the data into two blocks consisting of nitrate supplemented and nitrate deprived samples. The second C-PCA model (Fig. 5c and d) arranged the data into three blocks consisting of leaf base, mid leaf, and leaf tip. The multiblock C-PCA scores plot (Fig. 5a) gave distinct clustering patterns for all three leaf regions within the two blocks corresponding to the presence or absence of nitrate, the multiblock C-PCA scores plot (Fig. 5c) also gave distinct clustering patterns for plants grown in the presence or absence of nitrate within the three blocks corresponding to each leaf section, and thus the respective PC loadings were derived (Fig. 5b and d) and further investigated. Secondly variable selection analyses using the univariate Wilcoxon rank-sum test were performed (Table S1). Each of the three leaf tissue sections were compared under the two respective nitrate conditions, and each respective tissue section was compared between the two nitrate conditions. Finally, BN analyses were performed upon all features where a metabolite identification was attained via library matching and focused upon comparisons of the leaf base (fully heterotrophic) and tip (fully autotrophic) in the absence or presence of nitrate.

Mentions:
A total of 115 metabolite features were detected by GC-TOF/MS in wheat primary leaves grown in the presence or absence of nitrate, of which a total of 51 metabolites were identified by library matching (Table S1). Chemometric analysis of the GC-TOF/MS metabolite profiles focused upon the selection of differentially expressed metabolites that revealed significant trends either between the leaf regions or in response to growing the plant in the presence or absence of nitrate. Three approaches to the data mining were applied. First multiblock Consensus (C)-PCA was applied (Biais et al., 2009; Smilde et al., 2003; Westerhuis et al., 1998; Xu and Goodacre, 2012), where models combine several different but potentially connected data sets (called “blocks”), with emphasis upon modelling the “common trend” between the blocks. The sample distribution of each individual block are shown in their respective “block scores” plot and the contribution of metabolites in relation to the observed trend are shown in their “block loadings” plot (Biais et al., 2009). The first C-PCA model (Fig. 5a and b) arranged the data into two blocks consisting of nitrate supplemented and nitrate deprived samples. The second C-PCA model (Fig. 5c and d) arranged the data into three blocks consisting of leaf base, mid leaf, and leaf tip. The multiblock C-PCA scores plot (Fig. 5a) gave distinct clustering patterns for all three leaf regions within the two blocks corresponding to the presence or absence of nitrate, the multiblock C-PCA scores plot (Fig. 5c) also gave distinct clustering patterns for plants grown in the presence or absence of nitrate within the three blocks corresponding to each leaf section, and thus the respective PC loadings were derived (Fig. 5b and d) and further investigated. Secondly variable selection analyses using the univariate Wilcoxon rank-sum test were performed (Table S1). Each of the three leaf tissue sections were compared under the two respective nitrate conditions, and each respective tissue section was compared between the two nitrate conditions. Finally, BN analyses were performed upon all features where a metabolite identification was attained via library matching and focused upon comparisons of the leaf base (fully heterotrophic) and tip (fully autotrophic) in the absence or presence of nitrate.

Bottom Line:
Gas Chromatography-Time of Flight/Mass Spectrometry (GC-TOF/MS) combined with multivariate and univariate analyses, and Bayesian network (BN) analysis, distinguished different tissues and confirmed the physiological switch from high rates of respiration to photosynthesis along the leaf.In plants grown in the presence of nitrate there was reduced levels of a number of sugar metabolites in the leaf base and an increase in maltose levels, possibly reflecting an increase in starch turnover.The value of using this combined metabolomics analysis for further functional investigations in the future are discussed.